{
 "metadata": {
  "name": "",
  "signature": "sha256:260b69879f27dbe4f6277bd7396f1b36e8ea65faf88a3853989c56224c08339a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rockfish2.extensions.cps.model import CPSModel"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = CPSModel()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4.0\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1127f9c90>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.FMAX = 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.isolayers"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4.0\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[(0.0, 1.7999999999999998),\n",
        " (0.375, 2.1999999999999997),\n",
        " (0.5, 2.3574999999999999),\n",
        " (1.075, 2.4725000000000001),\n",
        " (1.6499999999999999, 2.5874999999999995),\n",
        " (2.2249999999999996, 2.7024999999999997),\n",
        " (2.7999999999999998, 2.8174999999999999),\n",
        " (3.375, 2.9325000000000001),\n",
        " (3.9499999999999997, 2.9950000000000001)]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = np.arange(0, 500, 10)\n",
      "npts = len(z)\n",
      "v = 1500 + 0.1 * np.arange(npts) + (2 * 0.5) * (np.random.rand(npts) - 0.5)\n",
      "\n",
      "model.ZV = zip(z, v)\n",
      "\n",
      "model.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "490\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ccw9kZ+sCcEk/6t6muP37YcIE+P3v4cQT4dFH7aIf3XQmUj6FXgjt3QtPPgnjxkGvXvDs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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1128506d0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.z[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "4.0"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}